The detection of problems in a fluid delivery system is generally problematic. The most common problems leading to catastrophic failures in such systems, such as minor leaks, contamination, or pump or other equipment breakdown, are generally undetectable until a severe failure occurs, often resulting in large costs associated not only with the ensuing repairs, but also associated with the subsequent clean up of the material released as a result of the failure of the fluid delivery system. In many cases, clean up costs can escalate quickly, especially for potentially volatile or hazardous substances, such as fuel products or cleaning products. However, clean up costs comprise only one aspect of the financial loss to the business. In industries relying on fluid delivery systems, a catastrophic event represents not only a repair and clean up cost associated with the event, but also a loss on the goods being transported in the fluid delivery system. Therefore, businesses involved in such an enterprise are often seeking ways to minimize such types of losses by attempting to predict when catastrophic failures will occur and planning accordingly by exchanging parts out on a regular basis or budgeting for such events. However, even such measures do not always protect the business adequately, as the result and frequency of such events is often unpredictable.
In general, in order to prevent failures, businesses rely on careful monitoring of a fluid delivery system in order to detect any variation in the performance of the system. Such monitoring of fluid delivery systems generally comprises monitoring of the various components at all times. For example, pressure gauges may be installed at various points in the delivery system. Additionally, equipment performance, such as pump temperature or pump rotation speed, may also be monitored. Another method of monitoring fluid delivery systems is the manual inspection of the various components of the system. However, a manual inspection of the various components can be not only time-consuming, but also difficult where components of the system may be installed such that a thorough inspection is physically difficult to accomplish. In either case, once a problem is detected, the system is shutdown and the problem is verified and repaired if necessary.
However, the difficulty in using such systems is that any subtle signals that may signal an impending failure are often difficult, if not impossible to discern from the normal variation in performance of the system. Furthermore, these types of monitoring systems rely on statistical analysis and action is generally only taken when the data being monitored exceeds a pre-determined tolerance range or the calculated useful lifetime of a component has elapsed. Therefore, such systems are incapable of detecting subtle changes that may be precursors of a severe failure.
One method of differentiating between normal fluctuations and indicators of impending failure is an extended analysis of the monitored data. Experiments in many fields have found that patterns of impending damage in many types of networks start to form hours, perhaps days before a crisis situation occurs. The method of detecting these patterns in such networks has been very limited until recent years. It also been demonstrated that continuous pattern sampling and analysis can show that even for systems only demonstrating apparently random fluctuations, once a problem exists in a network, the underlying organizing patterns associated with a failure will eventually reach a terminal, perhaps crisis situation.
For example, studies of the human brain show that the natural disharmonic state of human brainwaves tends to harmonize to a single frequency pattern prior to the occurrence of some types of seizures. In such individuals, it has been demonstrated that the movement of the brain to such a harmonious state can sometimes be detected hours, even days, before a seizure episode.
In a fluid delivery system, the same phenomena can occur. However, detecting such problems in real time and identifying the failure point is problematic. Even if data from various existing monitoring devices could be collected and analyzed, because of the subtle variations sought to be detected, existing instrumentation, such as pressure gauges, flow meters, and thermometers, is often insufficient. Furthermore, when dealing with fluid delivery lines that extend over long distances, perhaps over hundreds of miles, the cost of constructing, maintaining, installing, and monitoring such devices can be costly and cumbersome. Therefore, there is a need for utilizing newer technologies, capable of deployment over long distances and having lower cost of operation, such as miniature sensors, wireless data acquisition, and advanced computing methods, for use in failure prediction systems for fluid delivery systems.